1. Field of the Invention
This invention relates generally to the identification and classification of seismic sources using transient signals recorded passively with geophones or seismometers. More specifically, the invention derives seismic event classification probabilities by computing a time-frequency distribution, filtering out 2-dimensional noise, computing a binary and then a shift-invariant representation, and finally employing a self-organizing neural network for source identification.
2. Description of Related Art
Monitoring networks of seismic stations detect hundreds of thousands of seismic events annually. A variety of types of sources are responsible for these events which are detected and recorded by world wide seismic networks. Typical seismic sources include earthquakes, underground nuclear explosions, mining shots, cultural activities such as moving trucks or trains, and natural noise sources such as the wind, ocean waves, or the breaking of glaciers. To verify nuclear test ban treaties, all events detected by monitoring networks must be analyzed. Because of the large number of events that need to be identified, the problem requires an automated analysis platform. The various methods used in prior knowledge-based or expert systems have been only partially successful in automating seismic signal interpretation. This is because data characteristics vary significantly from event to event and because the solution is difficult to describe with a finite set of rules that are commonly used in conventional knowledge-based expert systems.
Different systems have been developed which classify and identify seismic sources. The following patents and articles describe these systems.
U.S. Pat. No. 4,713,775 is directed to an intelligent assistant system for using and operating computer system capabilities to solve problems. The patent uses some concepts of artificial intelligence to solve problems, however, the seismic event classification system. (SECs) described is an invention for interpreting seismic events. SECs interpret an event in the presence of varying noise by incorporating signal processing, neural networks, image understanding and a production rule system. The problem with static correction of seismic reflection data described in the patent is a highly specialized algorithm for a completely different problem than that of seismic event interpretation.
U.S. Pat. No. 4,939,648 is directed to a method and apparatus for monitoring well logging information. The patent uses signal processing on time series signals. SECs use a neural network to do pattern recognition. The patent uses observations related to situations in a knowledge base to do pattern recognition. The problem addressed in the patent is specific to well logging instrumentation and analysis.
An article by J. Bitto et al., entitled "Seismic Event Discrimination Using Neural Networks," 23rd Asilomar Conference on Signals, Systems, and Computers, Vol. 1, pp. 326-330, November 1989, describes a method used to discriminate between two known classes of events, for example earthquakes and nuclear tests. It describes use of a one-dimensional correlation of the data with supervised back propagation. SECs interpret any seismic event in the presence of varying noise by incorporating two-dimensional spectral estimation of a non-stationary process, making use of an unsupervised self-organizing neural network, using image understanding and production rule systems.
In the article by H. Liu, entitled "A Rule-Based System for Automatic Seismic Discrimination," Pattern Recognition, Vol. 18, No. 6, pp. 459-463, (1985), artificial intelligence technology is proposed. This paper is a proposal to solve a seismic discrimination problem using rules. The paper generally surveys the subject of seismic event discrimination for treaty verification, rule based systems, and a paragraph on pattern recognition.
In the article by I. Palaz et al., entitled "Waveform recognition Using Neural Networks," Geophysics: The Leading Edge of Exploration, Round Table, March 1990, supervised back propagation neural networks are proposed to recognize waveforms. The paper does not describe a method for interpretation of an event in the presence of noise by incorporating a number of new technologies.
SEC's combine a number of technologies and novel algorithms to interpret seismic events using pattern recognition techniques and high level reasoning. All of the above papers and patents are related by only a key word or by use of a particular technology.
Often, only human analysts are able to classify seismic events based on their experience of looking at many seismic events. The basic principle of this invention was motivated by how a human expert views the entire segment of a detected seismic waveform, using both seismic phase and the coda characteristics of the transient signals, to classify a given event.
It is proposed that with the proper representation of the seismic signal and by employing the machine learning properties of a self-organizing neural network, automation of seismic event classification can be achieved. The present invention provides such a method and article.